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Stochastic investigation of daily air temperature extremes from a global ground station network

机译：全球地面站网络每日空气温度极值的随机调查

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Near-surface air temperature is one of the most widely studied hydroclimatic variables, as both its regular and extremal behaviors are of paramount importance to human life. Following the global warming observed in the past decades and the advent of the anthropogenic climate change debate, interest in temperature's variability and extremes has been rising. It has since become clear that it is imperative not only to identify the exact shape of the temperature's distribution tails, but also to understand their temporal evolution. Here, we investigate the stochastic behavior of near-surface air temperature using the newly developed estimation tool of Knowable (K-)moments. K-moments, because of their property to substitute higher-order deviations from the mean with the distribution function, enable reliable estimation and an effective alternative to order statistics and, particularly for the outliers-prone distribution tails. We compile a large set of daily timeseries (30-200 years) of average, maximum and minimum air temperature, which we standardize with respect to the monthly variability of each record. Our focus is placed on the maximum and minimum temperatures, because they are more reliably measured than the average, yet very rarely analyzed in the literature. We examine segments of each timeseries using consecutive rolling 30-year periods, from which we extract extreme values corresponding to specific return period levels. Results suggest that the average and minimum temperature tend to increase, while overall the maximum temperature is slightly decreasing. Furthermore, we model the temperature timeseries as a filtered Hurst-Kolmogorov process and use Monte Carlo simulation to produce synthetic records with similar stochastic properties through the explicit Symmetric Moving Average scheme. We subsequently evaluate how the patterns observed in the longest records can be reproduced by the synthetic series.
机译：近表面空气温度是最广泛研究的循环变量之一，因为其常规和极端行为都对人类生命至关重要。在过去几十年中观察到的全球变暖之后和人为气候变化辩论的出现，对温度变异性和极端的兴趣一直在上升。因此，很明显，它不仅必须识别温度的分布尾部的精确形状，还必须了解他们的时间进化。在这里，我们使用新开发的知识估计工具来研究近表面空气温度的随机行为（K-）矩。 K-MOCENTS，因为它们的财产用与分布函数的平均值替换高阶偏差，使得能够可靠的估计和有效的替代方案来订购统计，特别是对于异常值 - 易于分布尾部。我们编制一整套每日时间（30-200岁）的平均，最大和最小空气温度，我们在每月的每月可变性方面标准化。我们的重点放在最大和最低温度上，因为它们比平均值更可靠地测量，但在文献中非常漫不知。我们使用连续滚动30年期检查每个次数的段，从中提取与特定返回周期级别对应的极值值。结果表明，平均和最低温度趋于增加，而总体上的最高温度略有下降。此外，我们将温度级数模拟为过滤的赫斯特 - 克尔莫戈戈罗夫工艺，并使用Monte Carlo模拟，通过明确的对称移动平均方案制造具有相似随机性能的合成记录。我们随后评估如何通过合成系列再现在最长记录中观察到的模式。

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